High-dimensional statistical inference with general estimating equations is challenging and remains little explored. We study two problems in the area: confidence set estimation for multiple components of the model parameters, and model specifications tests. First, we propose to construct a new set of estimating equations such that the impact from estimating the high-dimensional nuisance parameters becomes asymptotically negligible. The new construction enables us to estimate a valid confidence region by empirical likelihood ratio. Second, we propose a test statistic as the maximum of the marginal empirical likelihood ratios to quantify data evidence against the model specification. Our theory establishes the validity of the proposed empirical likelihood approaches, accommodating over-identification and exponentially growing data dimensionality. Numerical studies demonstrate promising performance and potential practical benefits of the new methods.

Corona virus disease-19 (COVID-19) has substantially reduced human activities and the associated anthropogenic emissions. This study quantifies the effects of COVID-19 control measures on six major air pollutants over 68 cities in North China by a Difference in Relative-Difference method that allows estimation of the COVID-19 effects while taking account of the general annual air quality trends, temporal and meteorological variations, and the spring festival effects. Significant COVID-19 effects on all six major air pollutants are found, with NO2 having the largest decline (−39.6%), followed by PM2.5 (−30.9%), O3 (−16.3%), PM10 (−14.3%), CO (−13.9%), and the least in SO2 (−10.0%), which shows the achievability of air quality improvement by a large reduction in anthropogenic emissions. The heterogeneity of effects among the six pollutants and different regions can be partly explained by coal consumption and industrial output data.

We propose a double selection instrumental variable estimator for the endogenous treat- ment effects using both high-dimensional control variables and instrumental variables. It deals with the endogeneity of the treatment variable and reduces omitted variable bias due to imperfect model selection.

High-dimensional elliptical sliced inverse regression in non-Gaussian distributions, Journal of Business & Economic Statistics, in press.

We thank the authorsfor their new contribution to networkmodelling.Datareuse, encompassingmethods such as bootstrapping and cross-validation, is an area that to date has largely resisted obvious and rapid development in the network context. One of the major reasons is that mimicking the original sampling mechanisms is challenging if not impossible. To avoid deleting edges and destroying some of the network structure, the resampling strategy proposed in Li et al. (2020) based on splitting node pairs rather than nodes is therefore insightful and effective. Matrix completion is the key technique involved, with its use here providing a new perspective for network analysis.

The purpose of multimodal classification is to integrate features from diverse information sources to make decisions. The interactions between different modalities are crucial to this task. However, common strategies in previous studies have been to either concatenate features from various sources into a single compound vector or input them separately into several different classifiers that are then assembled into a single robust classifier to generate the final prediction. Both of these approaches weaken or even ignore the interactions among different feature modalities. In addition, in the case of class-imbalanced data, multimodal classification becomes troublesome. In this study, we propose a deep multimodal generative and fusion framework for multimodal classification with class-imbalanced data. This framework consists of two modules: a deep multimodal generative adversarial network (DMGAN) and a deep multimodal hybrid fusion network (DMHFN). The DMGAN is used to handle the class imbalance problem. The DMHFN identifies fine-grained interactions and integrates different information sources for multimodal classification. Experiments on a faculty homepage dataset show the superiority of our framework compared to several start-of-the-art methods.

Multimodal data consist of several data modes, where each mode is a group of similar data sharing the same attributes. Recognizing faculty homepages is essentially a multimodal classification problem in which a target faculty homepage is determined from three different information sources, including text, images, and layout. Conventional strategies in previous studies have been either to concatenate features from various information sources into a compound vector or to input them separately into several different classifiers that are then assembled into a stronger classifier for the final prediction. However, both approaches ignore the connections among different feature sets. We argue that such relations are essential to enhance multimodal classification. Besides, recognizing faculty homepages is a class imbalance problem in which the total number of samples of a minority class is far smaller than the sample numbers of other classes. In this study, we propose a multimodal generative and fusion framework for multimodal learning with the problems of imbalanced data and mutually dependent feature modes. Specifically, a multimodal generative adversarial network is first introduced to rebalance the dataset by generating pseudo features based on each mode and combining them to describe a fake sample. Then, a gated fusion network with the gate and fusion mechanisms is presented to reduce the noise to improve the generalization ability and capture the links among the different feature modes. Experiments on a faculty homepage dataset show the superiority of the proposed framework.

Principal component analysis (PCA) is widely used in various fields to reduce high dimensional data sets to lower dimensions. Traditionally, the first a few principal components that capture most of the variance in the data are thought to be important. Tipping and Bishop (1999) introduced probabilistic principal component analysis (PPCA) in which they assumed an isotropic error in a latent variable model. Motivated by a general error structure and incorporating the novel idea of ‘‘envelope” proposed by Cook et al. (2010), we construct principal envelope models (PEM) which demonstrate the possibility that any subset of the principal components could retain most of the sample’s information. The useful principal components can be found through maximum likelihood approaches. We also embed the PEM to a factor model setting to illustrate its reasonableness and validity. Numerical results indicate the potentials of the proposed method.

为独立同分布的离散型随机变量序列，其分布函数为F(x).得到了F(x)属于幂赋范极值分布吸引场 的条件矩刻画.

In this paper, we propose a penalized generalized empirical likelihood (PGEL) approach based on the smoothed moment functions Anatolyev (2005), Smith (1997), Smith (2004) for parameters estimation and variable selection in the growing (high) dimensional weakly dependent time series setting. The dimensions of the parameters and moment restrictions are both allowed to grow with the sample size at some moderate rates. The asymptotic properties of the estimators of the smoothed generalized empirical likelihood (SGEL) and its penalized version (SPGEL) are then obtained by properly restricting the degree of data dependence. It is shown that the SPGEL estimator maintains the oracle property despite the existence of data dependence and growing (high) dimensionality. We finally present simulation results and a real data analysis to illustrate the finite-sample performance and applicability of our proposed method.